Metadata-Version: 2.4
Name: luminamoon
Version: 1.0.3
Requires-Dist: numpy>=2.0.0 ; extra == 'numpy'
Requires-Dist: torch>=2.0.0 ; extra == 'torch'
Requires-Dist: triton>=3.0.0 ; sys_platform != 'win32' and extra == 'gpu'
Requires-Dist: triton-windows>=3.0.0 ; sys_platform == 'win32' and extra == 'gpu'
Requires-Dist: numpy>=2.0.0 ; extra == 'all'
Requires-Dist: torch>=2.0.0 ; extra == 'all'
Requires-Dist: triton>=3.0.0 ; sys_platform != 'win32' and extra == 'all'
Requires-Dist: triton-windows>=3.0.0 ; sys_platform == 'win32' and extra == 'all'
Requires-Dist: maturin>=1.15.0 ; extra == 'dev'
Requires-Dist: pytest>=9.0.0 ; extra == 'dev'
Requires-Dist: pyright>=1.1.400 ; extra == 'dev'
Requires-Dist: scipy>=1.15.0 ; extra == 'eval'
Requires-Dist: z3-solver>=5.3.0 ; extra == 'eval'
Provides-Extra: all
Provides-Extra: dev
Provides-Extra: eval
Provides-Extra: gpu
Provides-Extra: numpy
Provides-Extra: torch
License-File: LICENSE
Summary: High-Performance Numerical Optimization, Multimodal Dataset Streaming, and Formal Verification Suite for PROJECT LUMI
Author: Lumina Moon (cloverx-id)
Author-email: "Silver Moon (cloverxion)" <cloverxion@luminamoon.org>
License: Apache-2.0
Requires-Python: >=3.10
Description-Content-Type: text/markdown; charset=UTF-8; variant=GFM

# PROJECT LUMI

**LuminaMoon (`luminamoon`)** is the official reference implementation and central monorepo of **PROJECT LUMI**, providing low-precision numerical optimization, multimodal dataset streaming, and formal verification suites developed by **Lumina Moon ([@cloverx-id](https://github.com/cloverx-id))** and **Silver Moon ([@cloverxion](https://github.com/cloverxion))**.

Built with a modular architecture under the **PROJECT LUMI** specification, `luminamoon` connects specialized components for CPU/GPU hardware execution, low-latency streaming, and verified numerical stability:

## System Architecture

```mermaid
flowchart TD
    Shards["Dataset Sources (HF / PyTorch / Shards)"] --> DataLoader["LuminaMoon Data Engine (SIMD + StringTensor + XXH3)"]
    DataLoader --> TrainLoop["Training Loop (Batch Stream)"]
    DataLoader --> Checkpoint["128B Ping-Pong Checkpoint & WAL"]
    TrainLoop --> Checkpoint
    TrainLoop --> Dispatcher["LuminaV Hardware Dispatcher"]
    Eval["LSO-Eval Suite (Z3 SMT & Parity Gate)"] -.-> Dispatcher
    Dispatcher --> Triton["Triton AOT/JIT GPU (CUDA / ROCm)"]
    Dispatcher --> Vulkan["Vulkan 1.3/1.4 Compute (UMA / SPIR-V)"]
    Dispatcher --> SIMD["Rayon CPU SIMD (AVX-512 / AVX2)"]
    Dispatcher --> Scalar["Reference Scalar Core (Fallback)"]
```

## Projects & Components

| Project / Component | Path | Status | Description | Documentation |
| :--- | :--- | :--- | :--- | :--- |
| **LuminaV Optimizer** | [`optimizer/`](optimizer/README.md) | `Active` (v1.5.3) | Canonical LuminaV v1.5.3 optimizer engine with CPU SIMD (AVX-512/AVX2), Vulkan UMA, and Triton AOT/JIT kernels. | [Optimizer Guide](optimizer/README.md) |
| **LuminaMoon Data Engine** | [`data/`](data/README.md) | `Active` (v1.0.3) | Modular preprocessing engine: AVX-512/AVX2/NEON SIMD text normalization, dual-layout StringTensor (CPU StringView & GPU contiguous buffers), heuristic Shannon quality filtering, MinHash/XXH3-64 deduplication, PII masking, and 128B Ping-Pong crash recovery. | [Data Engine Guide](data/README.md) |
| **LSO-Eval Suite** | [`eval/`](eval/README.md) | `Active` | Formal verification suite: Z3 SMT solver invariants, 2D trajectory benchmarks, and numerical parity audits. | [Eval Guide](eval/README.md) |
| **Documentation** | [`docs/`](docs/README.md) | `Active` | Centralized documentation portal, developer internal blueprints (`docs/optimize/`, `docs/dataloader/`), and ecosystem version audits. | [Docs Portal](docs/README.md) |
| **Python Package** | [`python/luminamoon/`](python/luminamoon/__init__.py) | `Active` | Unified free-threaded (`abi3t`) Python namespace exposing LuminaV Optimizer, DataStreamLoader, and PyTorch/NumPy/HuggingFace interop. | [API Reference](docs/en/03_configuration_api.md) |

## Quickstart

### Installation

Install the compiled package wheel from PyPI:

```bash
pip install luminamoon
```

Or build from source using Maturin and the Rust 2024 toolchain:

```bash
git clone https://github.com/cloverx-id/luminamoon.git
cd luminamoon
pip install maturin
maturin develop --release
```

### Basic Usage (LuminaV Optimizer)

```python
import numpy as np
from luminamoon import LuminaV

# Initialize parameters
weights = np.random.randn(64, 10).astype(np.float32)
biases = np.zeros(10, dtype=np.float32)

# Instantiate LuminaV optimizer
optimizer = LuminaV([weights, biases], lr=1e-3, buffer=2)

# Execute optimization step with explicit gradients
grad_w = np.random.randn(64, 10).astype(np.float32)
grad_b = np.random.randn(10, dtype=np.float32)
optimizer.step(grads=[grad_w, grad_b])
```

For PyTorch deep learning workflows, see the [PyTorch Integration Guide](docs/en/01_getting_started.md#quickstart-pytorch-integration).

### LuminaV Algorithm & Data Engine Versioning (v1.5.3)

`luminamoon` natively implements the canonical **LuminaV v1.5.3** reference algorithm and provides explicit version introspection, locking, and backward-compatible modular resolution:

```python
from luminamoon import LuminaV
import luminamoon.data as lmd

# 1. Introspect default algorithm version (Canonical baseline: v1.5.3)
print(LuminaV.getVersion())   # "1.5.3"
print(lmd.get_version())       # "1.0.3"

# 2. Lock or select a specific version per-instance or globally
opt = LuminaV([weights, biases], lr=1e-3, version="1.5.3")
opt.setVersion("1.5.3")        # Instance-level version lock
LuminaV.setVersion("1.5.3")    # Class-level default

# 3. Dynamic modular resolution & legacy support
# Legacy/historical versions (e.g. "0.9.0", "1.0.0") are automatically resolved
# via on-demand local caching (~/.cache/luminamoon/versions/<ver>/) without failure.
opt_legacy = LuminaV([weights, biases], version="0.9.0")
```

### Basic Usage (LuminaMoon Data Engine: Fused Preprocessing & Zero-Copy Pipeline)

```python
from luminamoon.data import FusedPipeline, StringTensor, create_resumable_collate, ResumablePipeline

# 1. Ultra-fast fused single-pass preprocessing (>400k records/s) in Rust
pipeline = FusedPipeline(
    trim_whitespace=True,
    normalize_newlines=True,  # AVX-512 / AVX2 / NEON SIMD
    min_chars=10,
    min_entropy=2.0,
)
string_tensor = pipeline(["  sample 1\r\n", "sample 2 with proper prose text"])

# 2. Zero-copy export to Apache Arrow, Polars, and GPU-ready PyTorch buffers
polars_series = string_tensor.to_polars()
arrow_array = string_tensor.to_arrow()
offsets_tensor, payload_tensor = string_tensor.to_torch_gpu(device="cuda")

# 3. Stream dataset with async prefetching & 128B Ping-Pong crash-recovery
pipe = ResumablePipeline(
    source=raw_data,
    fused_pipeline=pipeline,
    batch_size=64,
    prefetch_batches=4,
    checkpoint_path="./cache/checkpoint.meta",
)
for batch in pipe:
    # Training step...
    pipe.commit()  # Atomically persists Dual-Slot A/B checkpoint and deduplication WAL
```

For detailed architecture, Shannon entropy heuristics, MinHash deduplication, and PyTorch `collate_fn` adapters, see the [Data Engine Guide](data/README.md).

## Verification & CI/CD

All pull requests and releases are validated across Linux, Windows, and macOS:

```bash
# Enforce zero-warning static analysis across the Rust workspace
cargo clippy --workspace --all-targets -- -D warnings

# Run Rust workspace unit and differential parity tests (Optimizer + DataLoader)
cargo test --workspace

# Run Python static type check
npx pyright python/luminamoon

# Run standalone NumPy, PyTorch, and DataLoader verification suites
python optimizer/tests/test_standalone_numpy.py
python optimizer/tests/test_end_to_end_training.py
python -B tests/test_data_modular.py
python -B tests/test_gpu_vs_cpu_strings.py

# Run LSO-Eval formal verification suite
python -m eval
```

## Developer Resources

For contributors and systems engineers building on or extending **PROJECT LUMI (`luminamoon`)**:
- **Optimizer Modular Documentation**: [`docs/optimize/`](docs/optimize/README.md)
- **Data Engine Modular Documentation**: [`data/`](data/README.md)
- **Optimizer Library Audits**: [`docs/library_audit/`](docs/library_audit/README.md)
- **Data Standalone Library Audits**: [`data/docs/library_audit/`](data/docs/library_audit/README.md)
- **Data Engine Architecture & User Guide**: [`data/README.md`](data/README.md)
- **User Documentation**: [`docs/en/`](docs/en/README.md)

## Authors & Maintenance

PROJECT LUMI (`luminamoon`) is engineered and maintained by:
- **Silver Moon ([@cloverxion](https://github.com/cloverxion))**
- **Lumina Moon ([@cloverx-id](https://github.com/cloverx-id))**

## License

Licensed under the Apache License, Version 2.0. See [LICENSE](LICENSE) for details.
